Abstract
Abstract. This paper addresses the problem of vehicle detection from an image sequence in difficult cases. Difficulties are notably caused by relatively small vehicles, vehicles that appear with low contrast or vehicles that drive at low speed. The image sequence considered here is recorded by a hovering helicopter and was stabilized prior to the vehicle detection step considered here. A practical algorithm is designed and implemented for this purpose of vehicle detection. Each pixel is identified firstly as either a background (road) or a foreground (vehicle) pixel by analyzing its gray-level temporal profile in a sequential way. Secondly, a vehicle is identified as a cluster of foreground pixels. The results of this new method are demonstrated on a test image-sequence featuring very congested traffic but also smoothly flowing traffic. It is shown that for both traffic situations the method is able to successfully detect low contrast, small size and low speed vehicles.
Highlights
AND TEST DATA DESCRIPTIONTraffic is a problem of all large cities and is continuously analyzed by both authorities and researchers
Previous problems with the vehicle detection of i) small size vehicles observed with scarce detail, ii) vehicles with low speed, and iii) low contrast vehicles, could be largely solved
Because each pixel is only compared to the previous value of the background, the procedure is done in a sequential way for every image
Summary
Traffic is a problem of all large cities and is continuously analyzed by both authorities and researchers. Driving behavior is the most influential element in traffic and still less is known about it. This is due to the lack of instruments to track many vehicles for a long period of time without their awareness in taking part in an experiment (Ossen, 2008)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
